Use of uncertainty regarding observations of traffic intersections to modify behavior of a vehicle

ABSTRACT

Methods and devices for using uncertainty regarding observations of traffic intersections to modify behavior of a vehicle are disclosed. In one embodiment, an example method includes determining a state of a traffic intersection using information from one or more sensors of a vehicle. The vehicle may be configured to operate in an autonomous mode. The method may also include determining an uncertainty associated with the determined state of the traffic intersection. The method may further include controlling the vehicle in the autonomous mode based on the determined state of the traffic intersection and the determined uncertainty.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/441,996 filed Apr. 9, 2012, the contents of which are herebyincorporated by reference.

BACKGROUND

Some vehicles are configured to operate in an autonomous mode in whichthe vehicle navigates through an environment with little or no inputfrom a driver. Such a vehicle may include one or more sensors that areconfigured to sense information about the environment. The vehicle mayuse the sensed information to navigate through the environment.

For example, if an output of the sensors is indicative that the vehicleis approaching an obstacle, the vehicle may navigate around theobstacle. Additionally, a vehicle may sense information about trafficsigns and traffic signals. For example, traffic signs may provideregulatory information or warning information while traffic signalspositioned at road intersections, pedestrian crossings, and otherlocations may be used to control competing flows of traffic.

SUMMARY

In one example aspect, a method is disclosed that includes determining astate of a traffic intersection using information from one or moresensors of a vehicle. The vehicle may be configured to operate in anautonomous mode. The method may also include determining an uncertaintyassociated with the determined state of the traffic intersection.According to the method, the vehicle may be controlled in the autonomousmode based on the determined state of the traffic intersection and thedetermined uncertainty.

In another example aspect, a non-transitory computer-readable medium isdisclosed having stored therein instructions executable by a computingdevice to cause the computing device to perform functions. The functionsmay include determining a state of a traffic intersection usinginformation from one or more sensors of a vehicle. The vehicle may beconfigured to operate in an autonomous mode. The functions may furtherinclude determining an uncertainty associated with the determined stateof the traffic intersection. Additionally, the functions may includecontrolling the vehicle in the autonomous mode based on the determinedstate of the traffic intersection and the determined uncertainty.

In yet another example aspect, a vehicle configured to be operated in anautonomous mode is disclosed. The vehicle may include one or moresensors, a memory, a processor, and instructions stored in the memoryand executable by the processor. The one or more sensors may beconfigured to obtain information associated with a traffic intersection,and the instructions may be executable to determine a state of thetraffic intersection based on the obtained information. The instructionsmay be further executable to determine an uncertainty associated withthe determined state of the traffic intersection. Additionally, theinstructions may be executable to control the vehicle in the autonomousmode based on the determined state of the traffic intersection and thedetermined uncertainty.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the figures and the followingdetailed description.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a block diagram of an example method of controlling a vehicle.

FIGS. 2A-2C are example conceptual illustrations of determining a stateof a traffic intersection.

FIG. 3 is an example state table for determining an uncertaintyassociated with a state of a traffic intersection.

FIG. 4 is an example flow chart for determining a state of a trafficintersection.

FIGS. 5A-5B are example conceptual illustrations of controlling avehicle.

FIG. 6 illustrates an example vehicle, in accordance with an embodiment.

FIG. 7 is a simplified block diagram of an example vehicle, inaccordance with an embodiment.

FIG. 8 is a schematic illustrating a conceptual partial view of anexample computer program product that includes a computer program forexecuting a computer process on a computing device, arranged accordingto at least some embodiments presented herein.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying figures, which form a part hereof. In the figures, similarsymbols typically identify similar components, unless context dictatesotherwise. The illustrative embodiments described in the detaileddescription, figures, and claims are not meant to be limiting. Otherembodiments may be utilized, and other changes may be made, withoutdeparting from the scope of the subject matter presented herein. It willbe readily understood that the aspects of the present disclosure asgenerally describe herein, and illustrated in the figures, can bearranged, substituted, combined, separated, and designed in a widevariety of different configurations, all of which are explicitlycontemplated herein.

A vehicle, such as a vehicle configured to operate autonomously, may beconfigured to determine a state of a traffic intersection. For example,the vehicle may use one or more sensors such as a camera and/or laser todetermine a state of a traffic intersection. In some instances, thetraffic intersection may be governed by a traffic signal or trafficsign, and information associated with the traffic signal or traffic signmay be used to determine the state of the traffic intersection.

Additionally, an estimate of the state of the traffic intersection maybe noisy or imperfect. For instance, sensors used to determine the stateof the traffic intersection may be noisy or locations of traffic signalsand traffic signs may change. Consequently, a determined state of thetraffic intersection may be imperfect, and an uncertainty associatedwith the determined state of the traffic intersection may be determined.A control system of the vehicle and/or a driver or passenger of thevehicle may use the determined state of the traffic intersection as wellas the uncertainty associated with the determined state to modifybehavior of the vehicle.

As an example, a vehicle may determine a state of a traffic signalcontrolling an upcoming traffic intersection. Using information from oneor more sensors, the vehicle may determine that the state of the trafficsignal is green, but an amount of noise may be associated with thedetermined state. For instance, the traffic signal may be partiallyoccluded or a color of the green traffic light may not be as sharplycontrasting as expected. In such an instance, behavior of the vehiclemay be modified such that the vehicle cautiously approaches the trafficintersection, and observes additional information about the trafficintersection before proceeding. Alternatively, if the uncertaintyassociated with the state of the traffic intersection is low, thevehicle may proceed with confidence through the traffic intersectionwithout traveling slowly. Other additional modifications for control ofthe vehicle based on an uncertainty associated with a determined stateof a traffic intersection are also possible.

FIG. 1 is a block diagram of an example method 100 of controlling avehicle. Method 100 shown in FIG. 1 presents an embodiment of a methodthat could be used with the vehicles described herein, for example, andmay be performed by a vehicle or components of a vehicle, or moregenerally by a server or other computing device. Method 100 may includeone or more operations, functions, or actions as illustrated by one ormore of blocks 102-106. Although the blocks are illustrated in asequential order, these blocks may also be performed in parallel, and/orin a different order than those described herein. Also, the variousblocks may be combined into fewer blocks, divided into additionalblocks, and/or removed based upon the desired implementation.

In addition, for the method 100 and other processes and methodsdisclosed herein, the block diagram shows functionality and operation ofone possible implementation of present embodiments. In this regard, eachblock may represent a module, a segment, or a portion of program code,which includes one or more instructions executable by a processor forimplementing specific logical functions or steps in the process. Theprogram code may be stored on any type of computer-readable medium, suchas, for example, a storage device including a disk or hard drive. Thecomputer-readable medium may include a non-transitory computer-readablemedium, for example, such as computer-readable media that store data forshort periods of time like register memory, processor cache, and RandomAccess Memory (RAM). The computer-readable medium may also includenon-transitory media, such as secondary or persistent long term storage,like read only memory (ROM), optical or magnetic disks, and compact-discread only memory (CD-ROM), for example. The computer-readable media mayalso be any other volatile or non-volatile storage systems. Thecomputer-readable medium may be considered a computer-readable storagemedium, a tangible storage device, or other article of manufacture, forexample.

In addition, for the method 100 and other processes and methodsdisclosed herein, each block may represent circuitry that is configuredto perform the specific logical functions in the process.

As shown, initially, at block 102, the method 100 includes determining astate of a traffic intersection using information from one or moresensors of a vehicle. In one example, the vehicle may be configured tooperate autonomously. The vehicle described with respect to FIGS. 6 and7 is one such example of a vehicle that may be configured to operateautonomously. In some instances, a control command as determined basedon the state of the traffic intersection may be used to assist a driverof the vehicle or provided as input to a control system of the vehicle.

In one example, determining a state of the traffic intersection mayinclude determining a state of a traffic signal. For example, one ormore sensors of the vehicle may be used to scan a target area todetermine a state of the traffic signal. The one or more sensors mayinclude imaging and/or non-imaging components. For example, varioustypes of cameras may be mounted in various configurations to the vehicleto capture an image of an upcoming area. In one instance, a camera maybe positioned to face straight ahead and mounted behind or near arear-view mirror. Additionally, a camera may capture a specific regionof interest, such as a 2040×1080 region, of a camera with a fixed lenswith a 30 degree field of view. The camera may be calibrated to detecttraffic signals at various distances to ensure a reasonable brakingdistance. In a further example, gain and shutter speeds of the cameramay be set to avoid saturation of traffic lights during the day and/ornight.

In another example, the one or more sensors may include athree-dimensional (3D) scanning device configured to determine distancesto surfaces of objects in an upcoming area. In one instance, astructured light projection device and camera may be used to determine athree-dimensional point cloud describing the upcoming area. In anotherinstance, a laser and/or radar device such as a LIDAR or laserrangefinder may scan the target area to determine distances to objects.In other instances, a stereo camera or time-of-flight camera may be usedfor range imaging.

Based on information from the one or more sensors, a traffic signal maybe detected. In some instances, a pattern, template, shape, or signaturethat is expected for traffic signals may be identified. As an example, atraffic signal classifier may find a pattern of red, yellow, and greenobjects with appropriate size and aspect ratios within an image. Anyexample image processing techniques may be used to identify one or moreportions of the image matching a known pattern. Template matching is onepossible example.

In an instance, in which the 3D position of a traffic signal is known, aprediction of where a traffic signal should appear in an image may beused to determine the state of the traffic signal. For example, based onan estimate of a position of the vehicle with respect to the 3D position(e.g., using a GPS), a predicted position for the traffic signal can beprojected into the image from of a camera of the vehicle. In oneexample, the predicted position may be an axis-aligned bounding boxwhich selects a portion of the image. However, other example regions orshapes are also possible. The portion of the image may then be analyzedto determine the state of the traffic signal.

For example, the predicted position may be processed to identifybrightly colored red or green objects, and the predicted position maychange as the position of the vehicle approaches the traffic signal. Insome instances, if a traffic signal is detected, a state of the trafficsignal may be determined. Determining the state of the traffic signalmay include determining which object in a pattern of red, yellow, andgreen objects of an image of a traffic signal is illuminated. In oneinstance, an image processing method may be used to determine adistinction between brightness of the red, yellow, and green objects todetermine an object with the highest brightness. The object with thehighest brightness may be assumed to be on, for example. In an instancein which multiple traffic signals are detected, a state of a trafficsignal corresponding to a traffic lane of the vehicle may be determined.It will be understood that the specific structure of red, yellow, andgreen lights is merely an example. Traffic signals may have varied andsometimes complex geometries and these additional geometries may also bedetected.

In an example in which a three-dimensional point cloud is determined, anobject in the 3D point cloud having a shape that is expected for trafficsignals may be detected. For example, 3D point cloud based objectrecognition systems may be used to identify features describing interestpoints within the 3D point cloud. The features may subsequently becompared to features expected for a 3D model of one or more types oftraffic signals to recognize groups of interest points as objects withinthe 3D point cloud that are traffic signals. Given a recognized trafficsignal, color information (e.g., an image) associated with the locationof the 3D object may be analyzed to determine the state of the trafficsignal.

Information about other types of traffic signals and traffic signs mayalso be used to determine a state of a traffic intersection. Forexample, a traffic intersection may be governed by one or more trafficsignals having flashing red and/or yellow lights. In an instance inwhich a four-way traffic intersection is governed by a traffic signalhaving flashing red lights and/or stop signs, determining the state ofthe traffic intersection may involve determining which of severalpossible vehicles at the traffic intersection has precedence. Forexample, object recognition via classification of arbitrary objectstracks may enable the vehicle to recognize and determine locations ofvehicles, pedestrians, and/or bicycles based on 3D data. If a track ofan object in the 3D data is classified as a vehicle, a processor may beable to determine when or if a vehicle is present at a four-way stop.Accordingly, precedence for the traffic intersection may bestraightforwardly determined based on priority or right of way forvarious scenarios. Other example object recognition methods based on 2Dimages and/or 3D data are also possible.

In some instances, an amount of noise may be associated with adetermined state of a traffic intersection. At block 104, the method 100includes determining an uncertainty associated with the determined stateof the traffic intersection. In one example, the uncertainty associatedwith the determined state may be based on a confidence in a state of atraffic signal. For example, given an amount of noise associated with asensor, a state of a traffic signal may be determined with a givenconfidence level.

In another example, the uncertainty associated with a traffic signal maybe based on clarity of a traffic signal in an image. In some instances alocation and/or orientation of the traffic signal may change, impactingthe clarity of the traffic signal in an image. For instance, the natureof a structure of a traffic signal may cause the orientation to change(e.g., a traffic signal may swing when suspended from a cable, vibrate,etc.) or a traffic signal may be moved to a temporary location due toroad construction. In another instance, the brightness or clarity of animage may be affected based on a position of the sun at various times ofthe day or night. In another example, an image of a traffic signal ortraffic sign may be noisy in various weather conditions such as fog,snow, rain, etc. In still another example, a portion of the trafficsignal may be partially obstructed in an image. For example, a tree oranother vehicle may obstruct a portion of a traffic signal in an image.

In some examples, the clarity of the traffic signal may be compared to apredetermined threshold (e.g., a hue, brightness, etc.) to determine anuncertainty associated with a state of the traffic signal. In otherexamples, image processing may be used to determine an amount ofcontrast (e.g., a difference in luminance and/or color) between a green,yellow, or red object of a detected traffic signal and other portions ofthe image. The uncertainty may be related to the amount of contrastversus an expected amount of contrast for the green, yellow, or redobject.

In other examples, the uncertainty may be determined based on ahistorical ability to determine the state of the traffic intersectionbased on the traffic signal. For instance, information for detectedtraffic signals may be logged and analyzed to determine statisticalmodels for the traffic signals. In one example, a statistical model mayindicate that a state of a given traffic signal is detectable 98% of thetime. The example amount of time for the traffic signal may be used todetermine an uncertainty for the traffic signal. For instance, a trafficsignal whose state is detectable 95% of the time may have a loweruncertainty than a traffic signal whose state is detectable less than95% of the time. In another example, a statistical model may indicatethat a state of another traffic signal is often indiscernible at a giventime of day. In still another example, a statistical model may indicatethat an uncertainty for a state of a given traffic signal is above athreshold when a vehicle is traveling at a speed that is greater than anamount (e.g., 35 mph).

At block 106, the method 100 includes controlling the vehicle in theautonomous mode based on the determined state of the trafficintersection and the determined uncertainty. In some examples, apresumed state of the traffic intersection may be selected by aprocessing component based on the determined state of the trafficintersection and the determined uncertainty. As an example, if thedetermined uncertainty is low, and the state of the traffic signal isgreen, the vehicle may proceed through the traffic intersection andmaintain its speed. Alternatively, if the uncertainty associated with adetermined state of a traffic signal is high, the vehicle may determineadditional information from a variety of different sources ofinformation.

For instance, the vehicle may gather information (e.g., determined inreal-time or previously from the one or more sensors or additionalsensors) to confirm the state of the traffic signal. The information mayindicate behavior of other vehicles, such as vehicles traveling in thesame or opposite direction, or cross-traffic. In one example, thevehicle may determine information such as changes in speed oracceleration, or distances to vehicles in front of and/or behind thevehicle. A vehicle in front of the vehicle that is braking may beindicative of a traffic signal that is yellow or red. The informationmay also indicate behavior of pedestrians in a crosswalk. For example, apedestrian walking in a crosswalk that is perpendicular to a directionof travel of the vehicle may be indicative of a traffic signal that isred. In some instances, the vehicle may move to an adjusted position anddetermine additional information. For instance, the vehicle may move toan adjacent lane that is also a path in the same direction through thetraffic intersection and determine information about a state of thetraffic signal or a state of a different traffic signal.

In other examples, the vehicle may request information from a passengeror driver of the vehicle regarding the state of a traffic intersectionor a traffic signal. Based on information received from the passenger ordriver, the behavior of the vehicle may be modified. In other examples,the vehicle may tap into a network of the traffic signal or multipletraffic signals to determine information about the state of the trafficintersection. For example, the vehicle may receive information about thestate of a traffic signal or a traffic intersection from a wirelesstransmitter or transceiver located at the traffic signal, at a wirelesstower, on a satellite, on another vehicle, etc.

Thus, the example method 100 may enable controlling a vehicle based on astate of a traffic intersection and a determined uncertainty associatedwith the state of the traffic intersection. A number of exampleimplementations of the method 100 are described below in connection withFIGS. 2A-5B. For purposes of illustration, multiple exampleimplementations are described. It is to be understood, however, that theexample implementations are illustrative only and are not meant tolimiting. Other example implementations are possible as well.

FIGS. 2A-2C are example conceptual illustrations of determining a stateof a traffic intersection. For example, FIGS. 2A-2C illustrate a topview of a vehicle 200 and two perspective views from a position of thevehicle 200. In some examples, the vehicle 200 may scan a target area todetermine information associated with a state of a traffic intersection.For example, information within a field of view 202 of the vehicle 200may be obtained. In one instance, an imaging component and/or radarcomponent of the vehicle may be used to obtain information within thefield of view 202. The imaging component and radar component may bemounted behind or adjacent to a rear-view mirror, such that in ascenario in which the vehicle 200 includes a driver and/or passenger(s),the imaging component or radar component minimally obstructs a field ofview of the deriver and/or passenger(s). In another scenario, theimaging component and/or the radar component may be mounted on the topof the vehicle 200.

As shown in FIG. 2B, in one example, the vehicle 200 may obtain an imageof an area 204B. The image may include 2D and/or 3D information. In someexamples, the area 204B may include one or more traffic signals and mayoptionally be predetermined based on known locations of traffic signalsand a location and orientation of the vehicle 200.

In some examples, the clarity of an image of the area 204B may beaffected by lighting conditions. As shown in FIG. 2B, the position ofthe sun may be located behind a position of a traffic signal at sometimes of the day. This may lead to an uncertainty that is higher thanuncertainties for other times of the day when the clarity of the imageis not affected by the position of the sun.

As shown in FIG. 2C, an image of an area 204C may also be obstructed byphysical objects. For example, a tree limb may obstruct a portion of atraffic signal in an image of the area 204C. In one example, a 3D imageof the area 204C may reveal objects at multiple distances from theposition of the vehicle in a region for which a traffic signal isexpected. For example, a first distance may be determined to a point inthe area 204C that is closer to the vehicle than a group of points inthe area 204C that are in the shape of a portion of a traffic signal.This may be indicative of an object that is obstructing a view of thetraffic signal. In some instances, a high uncertainty may be determinedfor instances in which an object is obstructing a view of the trafficsignal.

In one example scenario, respective states of multiple traffic signalsof a traffic intersection may be determined. For example, two or moretraffic signals may control a flow of traffic for two or more lanes ofvehicles moving in a given direction. In an instance in which states aredetermined for multiple traffic signals, an uncertainty associated witha state of the traffic intersection may be determined based on themultiple states.

FIG. 3 is an example state table 300 for determining an uncertaintyassociated with a state of a traffic intersection. The state table 300may indicate an uncertainty that is determined for given states of atraffic signal A and a traffic signal B. In one example, the trafficsignal A and the traffic signal B may control the flow of traffic fortwo adjacent lanes of travel which follow the same path through atraffic intersection.

As shown in FIG. 3, given a state 302 in which the state of trafficsignal A and the state of traffic signal B are both red, an output 304of a low uncertainty may be determined. In another instance, a state ofa first traffic signal for a current lane of the vehicle may be green,and a state of another traffic signal for an adjacent lane may be red.According to the state table 300, a high uncertainty may be determined.In one example, the determined uncertainty may be high such that thebehavior of the vehicle is modified in a conservative manner (e.g.,preventing a dangerous crossing of a traffic intersection).

In some examples, an uncertainty associated with a determined state of atraffic intersection may be weighted by an implication of a meaning ofthe determined state. For instance, given that two lanes of trafficwhich follow the same path through a traffic intersection are oftencontrolled in a similar manner, the scenario in which a determined stateof a first traffic signal is red and a determined state of a secondtraffic signal is green is unlikely to occur. If any informationindicates that a state of an adjacent traffic signal may be red while adetermined state of a first traffic signal is green, a high uncertaintymay be determined for the state of the traffic intersection, and thevehicle may determine additional information about the state of thetraffic intersection. In some examples, if a vehicle has any uncertaintyat all about a state of a traffic intersection, it may be better to stopor slow the vehicle in order to determine additional information priorto proceeding through the traffic intersection, and control of thevehicle may be modified accordingly.

In another example, if a state of traffic signal A or a state of trafficsignal B is unknown, a high uncertainty may be determined. A scenario inwhich a state of a traffic signal is undetectable may imply thatinformation associated with the traffic intersection has changed. Forexample, a location of a traffic signal may have been moved. Given thehigh uncertainty, control of the vehicle may be modified to determineadditional information about the state of the traffic intersection.

FIG. 4 is an example flow chart 400 for determining a state of a trafficintersection. As shown, at block 402, a state of a traffic intersectionmay be determined. For instance, an autonomous vehicle may useinformation from one or more sensors to determine a state of a trafficsignal, or determine a vehicle having precedence at a four-way stop.Additionally, at block 404, an uncertainty associated with thedetermined state of the traffic intersection may be determined. In oneexample, the uncertainty may be an amount of clarity of a state of atraffic signal within an image. In other instances, the uncertainty maybe based on an amount of noise of a sensor, or a historical ability todetermine a state of a traffic intersection using information associatedwith a traffic signal or traffic sign.

Subsequently, at block 406, a determination may be made whether thedetermined uncertainty is greater than a predetermined uncertaintythreshold. In one example, if the uncertainty is less than theuncertainty threshold, at block 408 the determined state may be selectedby a processing component of the vehicle as the state of the trafficintersection. This may be likened to selecting a presumed state of thetraffic intersection as a state of the traffic intersection, andcontrolling the behavior of the vehicle based on the presumed state.

In another example, if the uncertainty is greater than the uncertaintythreshold, at block 410, a different state than the determined state ofthe traffic intersection may be selected by the processing component.For example, the processing component may defer to a different state dueto the determined uncertainty associated with the determined state. Insome instances, the different state of the traffic intersection may be amore restrictive or conservative state than the determined state of thetraffic intersection.

As an example, a state of a traffic signal may be detected to determinethe state of the traffic intersection, and the state of the trafficsignal may be green. A presumed state of the traffic intersection may bethat traffic is flowing through the traffic intersection via a path inwhich the vehicle is located. However, the determined state of thetraffic signal may include an amount of uncertainty that is greater thanthe threshold. Accordingly, a more restrictive state than the presumedstate may be selected, and control of the vehicle may be modified basedon the determined uncertainty such that the vehicle stops or slows downand determines additional information associated with the state of thetraffic intersection before proceeding according to the presumed stateand traveling through the traffic intersection. As an example, if adetermined state of a traffic signal is green and an uncertaintyassociated with the determined state is high, a presumed state of yellowor red may be selected

FIGS. 5A-5B are example conceptual illustrations of controlling avehicle. In some examples, a traffic intersection may be controlled by atraffic signal 500. As shown in FIGS. 5A-5B, a vehicle 502 may determinea state of the traffic signal 500 to determine a state of the trafficintersection. Additionally, an uncertainty associated with a determinedstate of the traffic signal 500 and associated traffic intersection maybe determined. In some instances, control of the vehicle may be modifiedbased on the determined state and the determined uncertainty.

As shown in FIG. 5A, in an instance in which an uncertainty associatedwith the traffic intersection is high, the vehicle 502 may move in adirection 504 to an adjusted position. For example, the vehicle 502 maymove slowly towards the traffic intersection. While the vehicle 502 ismoving, the vehicle 502 may determine additional information associatedwith the intersection using one or more sensors of the vehicle 502. Insome examples, the information may identify behavior of other vehicles,such as cross traffic approaching or traveling through an intersection.Based on the additional information, the vehicle 502 may re-determinethe state of the traffic intersection. For example, if cross traffic isnot stopping or yielding, the vehicle may stop or yield.

In some examples, the vehicle 502 may tap into a network of the trafficsignal or multiple traffic signals to determine information about thestate of the traffic intersection. For example, if an uncertaintyassociated with a traffic intersection is high, the vehicle 502 maycommunicate with a network via a wireless link 506 between the networkand a wireless communication system of the vehicle 502. For example, thevehicle 502 may communicate with a computing system of the trafficsignal via the wireless link 506. In one example, the vehicle 502 mayreceive information about the state of a traffic signal(s) via thewireless link 506 and compare the received information with thedetermined state of the traffic signal.

In some instances, the wireless communication system may include anantenna and a chipset for communicating with other vehicles, sensors, orother entities either directly or via a communication network. Thechipset or wireless communication system in general may be arranged tocommunicate according to one or more other types of wirelesscommunication (e.g., protocols) such as Bluetooth, communicationprotocols described in IEEE 802.11 (including any IEEE 802.11revisions), cellular technology (such as GSM, CDMA, UMTS, EV-DO, WiMAX,or LTE), Zigbee, dedicated short range communications (DSRC), and radiofrequency identification (RFID) communications, among otherpossibilities. The wireless communication system may take other forms aswell.

Systems in which example embodiments of the above example methods may beimplemented will now be described in greater detail. In general, anexample system may be implemented in or may take the form of a vehicle.The vehicle may take a number of forms, including, for example,automobiles, cars, trucks, motorcycles, buses, boats, airplanes,helicopters, lawn mowers, earth movers, snowmobiles, recreationalvehicles, amusement park vehicles, farm equipment, constructionequipment, trams, golf carts, trains, and trolleys. Other vehicles arepossible as well.

Further, another example system may take the form of non-transitorycomputer-readable medium, which has program instructions stored thereonthat are executable by at least one processor to provide thefunctionality described herein. An example system may also take the formof a vehicle or a subsystem of a vehicle that includes such anon-transitory computer-readable medium having such program instructionsstored thereon.

FIG. 6 illustrates an example vehicle 600, in accordance with anembodiment. In particular, FIG. 6 shows a Right Side View, Front View,Back View, and Top View of the vehicle 600. Although vehicle 600 isillustrated in FIG. 6 as a car, other embodiments are possible. Forinstance, the vehicle 600 could represent a truck, a van, a semi-trailertruck, a motorcycle, a golf cart, an off-road vehicle, or a farmvehicle, among other examples. As shown, the vehicle 600 includes afirst sensor unit 602, a second sensor unit 604, a third sensor unit606, a wireless communication system 608, and a camera 610.

Each of the first, second, and third sensor units 602-606 may includeany combination of global positioning system sensors, inertialmeasurement units, radio detection and ranging (RADAR) units, laserrangefinders, light detection and ranging (LIDAR) units, cameras, andacoustic sensors. Other types of sensors are possible as well.

While the first, second, and third sensor units 602 are shown to bemounted in particular locations on the vehicle 600, in some embodimentsthe sensor unit 602 may be mounted elsewhere on the vehicle 600, eitherinside or outside the vehicle 600. Further, while only three sensorunits are shown, in some embodiments more or fewer sensor units may beincluded in the vehicle 600.

In some embodiments, one or more of the first, second, and third sensorunits 602-606 may include one or more movable mounts on which thesensors may be movably mounted. The movable mount may include, forexample, a rotating platform. Sensors mounted on the rotating platformcould be rotated so that the sensors may obtain information from eachdirection around the vehicle 600. Alternatively or additionally, themovable mount may include a tilting platform. Sensors mounted on thetilting platform could be tilted within a particular range of anglesand/or azimuths so that the sensors may obtain information from avariety of angles. The movable mount may take other forms as well.

Further, in some embodiments, one or more of the first, second, andthird sensor units 602-606 may include one or more actuators configuredto adjust the position and/or orientation of sensors in the sensor unitby moving the sensors and/or movable mounts. Example actuators includemotors, pneumatic actuators, hydraulic pistons, relays, solenoids, andpiezoelectric actuators. Other actuators are possible as well.

The wireless communication system 608 may be any system configured towirelessly couple to one or more other vehicles, sensors, or otherentities, either directly or via a communication network. To this end,the wireless communication system 608 may include an antenna and achipset for communicating with the other vehicles, sensors, or otherentities either directly or via a communication network. The chipset orwireless communication system 608 in general may be arranged tocommunicate according to one or more other types of wirelesscommunication (e.g., protocols) such as Bluetooth, communicationprotocols described in IEEE 802.11 (including any IEEE 802.11revisions), cellular technology (such as GSM, CDMA, UMTS, EV-DO, WiMAX,or LTE), Zigbee, dedicated short range communications (DSRC), and radiofrequency identification (RFID) communications, among otherpossibilities. The wireless communication system 608 may take otherforms as well.

While the wireless communication system 608 is shown positioned on aroof of the vehicle 600, in other embodiments the wireless communicationsystem 608 could be located, fully or in part, elsewhere.

The camera 610 may be any camera (e.g., a still camera, a video camera,etc.) configured to capture images of the environment in which thevehicle 600 is located. To this end, the camera 610 may be configured todetect visible light, or may be configured to detect light from otherportions of the spectrum, such as infrared or ultraviolet light. Othertypes of cameras are possible as well. The camera 610 may be atwo-dimensional detector, or may have a three-dimensional spatial range.In some embodiments, the camera 610 may be, for example, a rangedetector configured to generate a two-dimensional image indicating adistance from the camera 610 to a number of points in the environment.To this end, the camera 610 may use one or more range detectingtechniques. For example, the camera 610 may use a structured lighttechnique in which the vehicle 600 illuminates an object in theenvironment with a predetermined light pattern, such as a grid orcheckerboard pattern and uses the camera 610 to detect a reflection ofthe predetermined light pattern off the object. Based on distortions inthe reflected light pattern, the vehicle 600 may determine the distanceto the points on the object. The predetermined light pattern maycomprise infrared light, or light of another wavelength. As anotherexample, the camera 610 may use a laser scanning technique in which thevehicle 600 emits a laser and scans across a number of points on anobject in the environment. While scanning the object, the vehicle 600uses the camera 610 to detect a reflection of the laser off the objectfor each point. Based on a length of time it takes the laser to reflectoff the object at each point, the vehicle 600 may determine the distanceto the points on the object. As yet another example, the camera 610 mayuse a time-of-flight technique in which the vehicle 600 emits a lightpulse and uses the camera 610 to detect a reflection of the light pulseoff an object at a number of points on the object. In particular, thecamera 610 may include a number of pixels, and each pixel may detect thereflection of the light pulse from a point on the object. Based on alength of time it takes the light pulse to reflect off the object ateach point, the vehicle 600 may determine the distance to the points onthe object. The light pulse may be a laser pulse. Other range detectingtechniques are possible as well, including stereo triangulation,sheet-of-light triangulation, interferometry, and coded aperturetechniques, among others. The camera 610 may take other forms as well.

In some embodiments, the camera 610 may include a movable mount and/oran actuator, as described above, that are configured to adjust theposition and/or orientation of the camera 610 by moving the camera 610and/or the movable mount.

While the camera 610 is shown to be mounted inside a front windshield ofthe vehicle 600, in other embodiments the camera 610 may be mountedelsewhere on the vehicle 600, either inside or outside the vehicle 600.

The vehicle 600 may include one or more other components in addition toor instead of those shown.

FIG. 7 is a simplified block diagram of an example vehicle 700, inaccordance with an embodiment. The vehicle 700 may, for example, besimilar to the vehicle 600 described above in connection with FIG. 6.The vehicle 700 may take other forms as well.

As shown, the vehicle 700 includes a propulsion system 702, a sensorsystem 704, a control system 706, peripherals 708, and a computer system710 including a processor 712, data storage 714, and instructions 716.In other embodiments, the vehicle 700 may include more, fewer, ordifferent systems, and each system may include more, fewer, or differentcomponents. Additionally, the systems and components shown may becombined or divided in any number of ways.

The propulsion system 702 may be configured to provide powered motionfor the vehicle 700. As shown, the propulsion system 702 includes anengine/motor 718, an energy source 720, a transmission 722, andwheels/tires 724.

The engine/motor 718 may be or include any combination of an internalcombustion engine, an electric motor, a steam engine, and a Stirlingengine. Other motors and engines are possible as well. In someembodiments, the propulsion system 702 could include multiple types ofengines and/or motors. For instance, a gas-electric hybrid car couldinclude a gasoline engine and an electric motor. Other examples arepossible.

The energy source 720 may be a source of energy that powers theengine/motor 718 in full or in part. That is, the engine/motor 718 maybe configured to convert the energy source 720 into mechanical energy.Examples of energy sources 720 include gasoline, diesel, propane, othercompressed gas-based fuels, ethanol, solar panels, batteries, and othersources of electrical power. The energy source(s) 720 could additionallyor alternatively include any combination of fuel tanks, batteries,capacitors, and/or flywheels. In some embodiments, the energy source 720may provide energy for other systems of the vehicle 700 as well.

The transmission 722 may be configured to transmit mechanical power fromthe engine/motor 718 to the wheels/tires 724. To this end, thetransmission 722 may include a gearbox, clutch, differential, driveshafts, and/or other elements. In embodiments where the transmission 722includes drive shafts, the drive shafts could include one or more axlesthat are configured to be coupled to the wheels/tires 724.

The wheels/tires 724 of vehicle 700 could be configured in variousformats, including a unicycle, bicycle/motorcycle, tricycle, orcar/truck four-wheel format. Other wheel/tire formats are possible aswell, such as those including six or more wheels. In any case, thewheels/tires 724 of vehicle 700 may be configured to rotatedifferentially with respect to other wheels/tires 724. In someembodiments, the wheels/tires 724 may include at least one wheel that isfixedly attached to the transmission 722 and at least one tire coupledto a rim of the wheel that could make contact with the driving surface.The wheels/tires 724 may include any combination of metal and rubber, orcombination of other materials.

The propulsion system 702 may additionally or alternatively includecomponents other than those shown.

The sensor system 704 may include a number of sensors configured tosense information about an environment in which the vehicle 700 islocated, as well as one or more actuators 736 configured to modify aposition and/or orientation of the sensors. As shown, the sensors of thesensor system include a Global Positioning System (GPS) 726, an inertialmeasurement unit (IMU) 728, a RADAR unit 730, a laser rangefinder and/orLIDAR unit 732, and a camera 734. The sensor system 704 may includeadditional sensors as well, including, for example, sensors that monitorinternal systems of the vehicle 700 (e.g., an O₂ monitor, a fuel gauge,an engine oil temperature, etc.). Other sensors are possible as well.

The GPS 726 may be any sensor configured to estimate a geographiclocation of the vehicle 700. To this end, the GPS 726 may include atransceiver configured to estimate a position of the vehicle 700 withrespect to the Earth. The GPS 726 may take other forms as well.

The IMU 728 may be any combination of sensors configured to senseposition and orientation changes of the vehicle 700 based on inertialacceleration. In some embodiments, the combination of sensors mayinclude, for example, accelerometers and gyroscopes. Other combinationsof sensors are possible as well.

The RADAR 730 unit may be any sensor configured to sense objects in theenvironment in which the vehicle 700 is located using radio signals. Insome embodiments, in addition to sensing the objects, the RADAR unit 730may additionally be configured to sense the speed and/or heading of theobjects.

Similarly, the laser rangefinder or LIDAR unit 732 may be any sensorconfigured to sense objects in the environment in which the vehicle 700is located using lasers. In particular, the laser rangefinder or LIDARunit 732 may include a laser source and/or laser scanner configured toemit a laser and a detector configured to detect reflections of thelaser. The laser rangefinder or LIDAR 732 may be configured to operatein a coherent (e.g., using heterodyne detection) or an incoherentdetection mode.

The camera 734 may be any camera (e.g., a still camera, a video camera,etc.) configured to capture images of the environment in which thevehicle 700 is located. To this end, the camera may take any of theforms described above.

The sensor system 704 may additionally or alternatively includecomponents other than those shown.

The control system 706 may be configured to control operation of thevehicle 700 and its components. To this end, the control system 706 mayinclude a steering unit 738, a throttle 740, a brake unit 742, a sensorfusion algorithm 744, a computer vision system 746, a navigation orpathing system 748, and an obstacle avoidance system 750.

The steering unit 738 may be any combination of mechanisms configured toadjust the heading of vehicle 700.

The throttle 740 may be any combination of mechanisms configured tocontrol the operating speed of the engine/motor 718 and, in turn, thespeed of the vehicle 700.

The brake unit 742 may be any combination of mechanisms configured todecelerate the vehicle 700. For example, the brake unit 742 may usefriction to slow the wheels/tires 724. As another example, the brakeunit 742 may convert the kinetic energy of the wheels/tires 724 toelectric current. The brake unit 742 may take other forms as well.

The sensor fusion algorithm 744 may be an algorithm (or a computerprogram product storing an algorithm) configured to accept data from thesensor system 704 as an input. The data may include, for example, datarepresenting information sensed at the sensors of the sensor system 704.The sensor fusion algorithm 744 may include, for example, a Kalmanfilter, a Bayesian network, or another algorithm. The sensor fusionalgorithm 744 may further be configured to provide various assessmentsbased on the data from the sensor system 704, including, for example,evaluations of individual objects and/or features in the environment inwhich the vehicle 700 is located, evaluations of particular situations,and/or evaluations of possible impacts based on particular situations.Other assessments are possible as well.

The computer vision system 746 may be any system configured to processand analyze images captured by the camera 734 in order to identifyobjects and/or features in the environment in which the vehicle 700 islocated, including, for example, traffic signals and obstacles. To thisend, the computer vision system 746 may use an object recognitionalgorithm, a Structure from Motion (SFM) algorithm, video tracking, orother computer vision techniques. In some embodiments, the computervision system 746 may additionally be configured to map the environment,track objects, estimate the speed of objects, etc.

The navigation and pathing system 748 may be any system configured todetermine a driving path for the vehicle 700. The navigation and pathingsystem 748 may additionally be configured to update the driving pathdynamically while the vehicle 700 is in operation. In some embodiments,the navigation and pathing system 748 may be configured to incorporatedata from the sensor fusion algorithm 744, the GPS 726, and one or morepredetermined maps so as to determine the driving path for vehicle 100.

The obstacle avoidance system 750 may be any system configured toidentify, evaluate, and avoid or otherwise negotiate obstacles in theenvironment in which the vehicle 700 is located.

The control system 706 may additionally or alternatively includecomponents other than those shown.

Peripherals 708 may be configured to allow the vehicle 700 to interactwith external sensors, other vehicles, and/or a user. To this end, theperipherals 708 may include, for example, a wireless communicationsystem 752, a touchscreen 754, a microphone 756, and/or a speaker 758.

The wireless communication system 752 may take any of the formsdescribed above.

The touchscreen 754 may be used by a user to input commands to thevehicle 700. To this end, the touchscreen 754 may be configured to senseat least one of a position and a movement of a user's finger viacapacitive sensing, resistance sensing, or a surface acoustic waveprocess, among other possibilities. The touchscreen 754 may be capableof sensing finger movement in a direction parallel or planar to thetouchscreen surface, in a direction normal to the touchscreen surface,or both, and may also be capable of sensing a level of pressure appliedto the touchscreen surface. The touchscreen 754 may be formed of one ormore translucent or transparent insulating layers and one or moretranslucent or transparent conducting layers. The touchscreen 754 maytake other forms as well.

The microphone 756 may be configured to receive audio (e.g., a voicecommand or other audio input) from a user of the vehicle 700. Similarly,the speakers 758 may be configured to output audio to the user of thevehicle 700.

The peripherals 708 may additionally or alternatively include componentsother than those shown.

The computer system 710 may be configured to transmit data to andreceive data from one or more of the propulsion system 702, the sensorsystem 704, the control system 706, and the peripherals 708. To thisend, the computer system 710 may be communicatively linked to one ormore of the propulsion system 702, the sensor system 704, the controlsystem 706, and the peripherals 708 by a system bus, network, and/orother connection mechanism (not shown).

The computer system 710 may be further configured to interact with andcontrol one or more components of the propulsion system 702, the sensorsystem 704, the control system 706, and/or the peripherals 708. Forexample, the computer system 710 may be configured to control operationof the transmission 722 to improve fuel efficiency. As another example,the computer system 710 may be configured to cause the camera 734 tocapture images of the environment. As yet another example, the computersystem 710 may be configured to store and execute instructionscorresponding to the sensor fusion algorithm 744. As still anotherexample, the computer system 710 may be configured to store and executeinstructions for displaying a display on the touchscreen 754. Otherexamples are possible as well.

As shown, the computer system 710 includes the processor 712 and datastorage 714. The processor 712 may comprise one or more general-purposeprocessors and/or one or more special-purpose processors. To the extentthe processor 712 includes more than one processor, such processorscould work separately or in combination. Data storage 714, in turn, maycomprise one or more volatile and/or one or more non-volatile storagecomponents, such as optical, magnetic, and/or organic storage, and datastorage 714 may be integrated in whole or in part with the processor712.

In some embodiments, data storage 714 may contain instructions 716(e.g., program logic) executable by the processor 712 to execute variousvehicle functions, including those described above in connection withFIG. 1. Further, data storage 714 may contain constraints 717 for thevehicle 700, which may take any of the forms described above. Datastorage 714 may contain additional instructions as well, includinginstructions to transmit data to, receive data from, interact with,and/or control one or more of the propulsion system 702, the sensorsystem 704, the control system 706, and the peripherals 708.

The computer system 702 may additionally or alternatively includecomponents other than those shown.

As shown, the vehicle 700 further includes a power supply 760, which maybe configured to provide power to some or all of the components of thevehicle 700. To this end, the power supply 760 may include, for example,a rechargeable lithium-ion or lead-acid battery. In some embodiments,one or more banks of batteries could be configured to provide electricalpower. Other power supply materials and configurations are possible aswell. In some embodiments, the power supply 760 and energy source 720may be implemented together, as in some all-electric cars.

In some embodiments, one or more of the propulsion system 702, thesensor system 704, the control system 706, and the peripherals 708 couldbe configured to work in an interconnected fashion with other componentswithin and/or outside their respective systems.

Further, the vehicle 700 may include one or more elements in addition toor instead of those shown. For example, the vehicle 700 may include oneor more additional interfaces and/or power supplies. Other additionalcomponents are possible as well. In such embodiments, data storage 714may further include instructions executable by the processor 712 tocontrol and/or communicate with the additional components.

Still further, while each of the components and systems are shown to beintegrated in the vehicle 700, in some embodiments, one or morecomponents or systems may be removably mounted on or otherwise connected(mechanically or electrically) to the vehicle 700 using wired orwireless connections.

The vehicle 700 may take other forms as well.

In some embodiments, the disclosed methods may be implemented ascomputer program instructions encoded on a non-transitorycomputer-readable storage media in a machine-readable format, or onother non-transitory media or articles of manufacture. FIG. 8 is aschematic illustrating a conceptual partial view of an example computerprogram product 800 that includes a computer program for executing acomputer process on a computing device, arranged according to at leastsome embodiments presented herein.

In one embodiment, the example computer program product 800 is providedusing a signal bearing medium 802. The signal bearing medium 802 mayinclude one or more programming instructions 804 that, when executed byone or more processors, may provide functionality or portions of thefunctionality described above with respect to FIGS. 1-7.

In some embodiments, the signal bearing medium 802 may encompass acomputer-readable medium 806, such as, but not limited to, a hard diskdrive, a Compact Disc (CD), a Digital Video Disk (DVD), a digital tape,memory, etc. Further, in some embodiments the signal bearing medium 802may encompass a computer recordable medium 808, such as, but not limitedto, memory, read/write (R/W) CDs, R/W DVDs, etc. Still further, in someembodiments the signal bearing medium 802 may encompass a communicationsmedium 810, such as, but not limited to, a digital and/or an analogcommunication medium (e.g., a fiber optic cable, a waveguide, a wiredcommunications link, a wireless communication link, etc.). Thus, forexample, the signal bearing medium 802 may be conveyed by a wirelessform of the communications medium 810.

The one or more programming instructions 804 may be, for example,computer executable and/or logic implemented instructions. In someexamples, a computing system such as the computing system 710 of FIG. 7may be configured to provide various operations, functions, or actionsin response to the programming instructions 804 being conveyed to thecomputing system 710 by one or more of the computer readable medium 806,the computer recordable medium 808, and/or the communications medium810.

It should be understood that arrangements described herein are forpurposes of example only. As such, those skilled in the art willappreciate that other arrangements and other elements (e.g. machines,interfaces, functions, orders, and groupings of functions, etc.) can beused instead, and some elements may be omitted altogether according tothe desired results. Further, many of the elements that are describedare functional entities that may be implemented as discrete ordistributed components or in conjunctions with other components, in anysuitable combination and location.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

What is claimed is:
 1. A method comprising: determining a state of atraffic intersection using information from one or more sensors of avehicle, wherein determining the state of the traffic intersectioncomprises determining a state of a traffic signal of the trafficintersection, and wherein the vehicle is configured to operate in anautonomous mode; determining an uncertainty associated with thedetermined state of the traffic intersection; and controlling thevehicle in the autonomous mode based on the determined state of thetraffic intersection and the determined uncertainty associated with thedetermined state of the traffic intersection.
 2. The method of claim 1,wherein the uncertainty associated with the determined state of thetraffic intersection comprises an uncertainty associated with thedetermined state of the traffic signal.
 3. The method of claim 2:wherein determining the state of the traffic signal comprises obtainingan image of the traffic signal; and wherein the uncertainty associatedwith the determined state of the traffic signal comprises a clarity ofthe traffic signal in the image.
 4. The method of claim 2, wherein theuncertainty associated with the determined state of the traffic signalcomprises a historical ability to determine the state of the trafficsignal.
 5. The method of claim 4, wherein the historical abilitycomprises a probability that the state of the traffic signal isdiscernable at a given time of day.
 6. The method of claim 4, whereinthe historical ability comprises a probability that the state of thetraffic signal is discernable when the vehicle is traveling at a speedthat is greater than a threshold speed.
 7. The method of claim 1:wherein determining the state of the traffic intersection furthercomprises determining another state of another traffic signal of thetraffic intersection; and wherein determining the uncertainty associatedwith the determined state of the traffic intersection comprisesdetermining the uncertainty associated with the determined state of thetraffic intersection based on both the state of the traffic signal ofthe traffic intersection and the other state of the other traffic signalof the traffic intersection.
 8. The method of claim 1, whereincontrolling the vehicle in the autonomous mode based on the determinedstate of the traffic intersection and the determined uncertaintyassociated with the determined state of the traffic intersectioncomprises controlling the vehicle according to a more restrictive statethan the determined state of the traffic intersection in response todetermining that the determined uncertainty associated with thedetermined state of the traffic intersection is greater than apredetermined uncertainty threshold.
 9. The method of claim 1, whereincontrolling the vehicle in the autonomous mode based on the determinedstate of the traffic intersection and the determined uncertaintyassociated with the determined state of the traffic intersectioncomprises (i) selecting a presumed state of the traffic intersectionbased on the determined state of the traffic intersection and thedetermined uncertainty associated with the state of the trafficintersection and (ii) controlling the vehicle in the autonomous modebased on the presumed state of the traffic intersection.
 10. The methodof claim 1, wherein controlling the vehicle in the autonomous mode basedon the determined state of the traffic intersection and the determineduncertainty associated with the state of the traffic intersectioncomprises, in response to determining that the determined uncertaintyassociated with the state of the traffic intersection is greater than apredetermined uncertainty threshold: moving the vehicle to an adjustedposition; obtaining from the adjusted position, using the one or moresensors of the vehicle, additional information that is indicative of thestate of the traffic intersection; and controlling the vehicle in theautonomous mode based on the determined state of the trafficintersection and the additional information.
 11. A non-transitorycomputer-readable medium having stored therein instructions executableby a computing device to cause the computing device to perform functionscomprising: determining a state of a traffic intersection usinginformation from one or more sensors of a vehicle, wherein determiningthe state of the traffic intersection comprises determining a state of atraffic signal of the traffic intersection, and wherein the vehicle isconfigured to operate in an autonomous mode; determining an uncertaintyassociated with the determined state of the traffic intersection; andcontrolling the vehicle in the autonomous mode based on the determinedstate of the traffic intersection and the determined uncertaintyassociated with the determined state of the traffic intersection. 12.The non-transitory computer-readable medium of claim 11, wherein theuncertainty associated with the determined state of the trafficintersection comprises an uncertainty associated with the determinedstate of the traffic signal.
 13. The non-transitory computer-readablemedium of claim 12: wherein determining the state of the traffic signalcomprises obtaining an image of the traffic signal; and wherein theuncertainty associated with the determined state of the traffic signalcomprises a clarity of the traffic signal in the image.
 14. Thenon-transitory computer-readable medium of claim 12, wherein theuncertainty associated with the determined state of the traffic signalcomprises a historical ability to determine the state of the trafficsignal.
 15. The non-transitory computer-readable medium of claim 11,wherein controlling the vehicle in the autonomous mode based on thedetermined state of the traffic intersection and the determineduncertainty associated with the state of the traffic intersectioncomprises, in response to determining that the determined uncertaintyassociated with the state of the traffic intersection is greater than apredetermined uncertainty threshold: moving the vehicle to an adjustedposition; obtaining from the adjusted position, using the one or moresensors of the vehicle, additional information that is indicative of thestate of the traffic intersection; and controlling the vehicle in theautonomous mode based on the determined state of the trafficintersection and the additional information.
 16. A vehicle configured tobe operated in an autonomous mode, comprising: one or more sensorsconfigured to obtain information associated with a traffic intersection;a memory; a processor; and instructions stored in the memory andexecutable by the processor to: determine a state of a trafficintersection based on the obtained information, wherein determining thestate of the traffic intersection comprises determining a state of atraffic signal of the traffic intersection, determine an uncertaintyassociated with the determined state of the traffic intersection, andcontrol the vehicle in the autonomous mode based on the determinedstated of the traffic intersection and the determined uncertaintyassociated with the determined state of the traffic intersection. 17.The vehicle of claim 16, wherein the uncertainty associated with thedetermined state of the traffic intersection comprises an uncertaintyassociated with the determined state of the traffic signal.
 18. Thevehicle of claim 17: wherein determining the state of the traffic signalcomprises obtaining an image of the traffic signal; and wherein theuncertainty associated with the determined state of the traffic signalcomprises a clarity of the traffic signal in the image.
 19. The vehicleof claim 17, wherein the uncertainty associated with the determinedstate of the traffic signal comprises a historical ability to determinethe state of the traffic signal.
 20. The vehicle of claim 16, whereincontrolling the vehicle in the autonomous mode based on the determinedstate of the traffic intersection and the determined uncertaintyassociated with the state of the traffic intersection comprises, inresponse to determining that the determined uncertainty associated withthe state of the traffic intersection is greater than a predetermineduncertainty threshold: moving the vehicle to an adjusted position;obtaining from the adjusted position, using the one or more sensors ofthe vehicle, additional information that is indicative of the state ofthe traffic intersection; and controlling the vehicle in the autonomousmode based on the determined state of the traffic intersection and theadditional information.